Notifications
Clear all
Topic starter
08/06/2026 4:28 pm
TL;DR: MCP standardises how AI agents connect to tools and data, while A2A standardises how agents discover and coordinate with each other, according to WorkOS. The governance problem is no longer just integration design: it is deciding where tool permissions end and inter-agent delegation begins.
NHIMG editorial — based on content published by WorkOS: MCP vs. A2A: Which AI agent protocol should you use?
Questions worth separating out
Q: How should security teams govern AI systems that use both MCP and A2A?
A: They should govern the workflow as a delegation chain, not as two separate technical choices.
Q: Why do MCP and A2A together create more identity risk than either one alone?
A: Because the risk is compounded across layers.
Q: What do IAM teams get wrong about agent-to-agent collaboration?
A: They often treat it as messaging infrastructure instead of delegated identity.
Practitioner guidance
- Map agentic delegation chains end to end Document which agents discover peers, which agents call tools, and where a human or policy gate can still interrupt the chain.
- Scope every MCP tool to a named business purpose Do not expose generic tool catalogs to agents without task boundaries.
- Treat Agent Cards as governance artifacts Review advertised capabilities the same way you would review service account privileges.
What's in the full article
WorkOS's full analysis covers the operational detail this post intentionally leaves for the source:
- Protocol-level examples showing how MCP tool manifests, JSON-RPC calls, and Agent Cards work in practice.
- Side-by-side decision points for when MCP, A2A, or both fit a specific enterprise workflow.
- Implementation-oriented guidance on secure delegation, consent, and permission scoping.
- Links to WorkOS resources on securing MCP servers and agent authorization patterns.
👉 Read WorkOS's MCP vs A2A comparison for agentic systems →
MCP vs A2A for agentic systems: what IAM teams should watch?
Explore further
08/06/2026 6:08 pm
MCP and A2A are not competing identity models, they are different control planes. MCP is about bounded tool reach, while A2A is about delegated coordination between peers. The governance mistake is to treat either protocol as a complete agent identity strategy. Practitioners should read them as separate layers of one expanding identity surface.
A few things that frame the scale:
- 33% of organisations report their AI agents have accessed inappropriate or sensitive data beyond their intended scope, according to AI Agents: The New Attack Surface report.
- Only 44% of organisations have implemented policies to govern AI agents, leaving a large share with documented behaviour but no formal control baseline.
A question worth separating out:
Q: How can organisations decide whether to start with MCP, A2A, or both?
A: Start with the control surface that matches the problem. Use MCP when the main requirement is safe access to tools and data. Use A2A when the core need is coordination across specialised agents. Use both when orchestration and execution are both present, then govern the combined identity path as one system.
👉 Read our full editorial: MCP vs A2A: what AI agent teams need for tool access
